import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import pandas as pd
pd.options.mode.chained_assignment = None 
import numpy as np
import platform as pl
import plotly.express as px
import plotly.graph_objects as go
import random
from datetime import datetime

# file = "logs_abdi_2.csv"
file = "dados_sim.csv"
escavadeira = 24
caminhao = 25

sep = '\\' if pl.uname().system == 'Windows' else '/'
df = pd.read_csv(f"..{sep}dados_csv{sep}{file}", encoding='iso-8859-1')
df.loc[:,'date_created'] = pd.to_datetime(df['date_created'])

litragem_cam = 210
litragem_esc = 345

Fator de Eficiência

O Fator de Eficiência leva em consideração os tempos efetivamente trabalhados pela escavadeira medido em uma hora, definindo sua relação em um indicador (E) adimensional. A periodicidade de aferição será ao final de cada turno de trabalho.

• E= Tempo efetivamente trabalhados (min) / 60 (min)

# df_data = df.loc[(df['imei'] == escavadeira)]
# df_data = df.loc[df['date_created'] < '2021-08-07 00:00:00']
df_data = df.copy()
df_data.loc[:,'date_created'] = pd.to_datetime(df_data['date_created'])
df_data['last_date'] = df_data['date_created'].shift(1)
df_data['interval'] = (df_data['date_created'] - df_data['last_date']) / np.timedelta64(1, 'm')
df_data = df_data.set_index('date_created')
df_data = df_data.loc['2021-08-06':]

df_total = df_data.copy()
df_data = df_data.loc[df_data['weight'] > 0]
# Agrupa por hora
df_data = df_data.groupby(pd.Grouper(freq='3600s')).sum()
df_total = df_total.groupby(pd.Grouper(freq='3600s')).max()

df_total['eficiencia'] = df_data['interval']
df_total['eficiencia'].fillna(0, inplace=True)
print(df_total.loc[df_total['eficiencia'] > 0, ['weight', 'eficiencia']])
fig = px.bar(df_total, y="eficiencia", title="Fator de Eficiencia")
fig.show()

                     weight  eficiencia
date_created                           
2021-08-06 08:00:00   640.0   11.283333
2021-08-06 09:00:00   640.0   12.683333
2021-08-06 10:00:00   631.0    9.250000
2021-08-06 11:00:00   592.0    4.416667
2021-08-06 12:00:00   606.0    5.183333
...                     ...         ...
2021-08-31 13:00:00   638.0   10.216667
2021-08-31 15:00:00   640.0    5.500000
2021-08-31 16:00:00   638.0   11.483333
2021-08-31 17:00:00   632.0    9.416667
2021-08-31 18:00:00   631.0    8.433333

[162 rows x 2 columns]

Rendimento

O Rendimento (R) mostrará a quantidade de material movimentado pelo equipamento em uma média diária.

• R = Somatório(pesos)/Dia (Kg/h)

df_rend = df_total.copy()
# Agrupa por dia
df_rend = df_rend.groupby(pd.Grouper(freq='86400s')).sum()
fig = px.bar(df_rend, y="weight", title="Rendimento")
fig.show()

Aferição do tempo de carga

Aferição do Tempo de Carga (Tc) de uma escavadeira para a realização de uma operação no canteiro de obra.

• Tc = Tempo ciclo de um equipamento (h)

import plotly.graph_objects as go

df_temp = df_data.copy()
df_temp.rename(columns = {'interval':'load_time'}, inplace = True)
df_temp = df_temp.loc[df_temp['weight'] > 0, ['weight', 'load_time']]
df_temp['weight'] = df_temp['weight']/1000

print(df_temp)
fig = go.Figure()
fig.add_trace(go.Bar(x=df_temp.index,
                y=df_temp['load_time'],
                name='Tempo em Min',
                marker_color='rgb(55, 83, 109)'
                ))
fig.add_trace(go.Bar(x=df_temp.index,
                y=df_temp['weight'],
                name='Carga em Ton.',
                marker_color='rgb(26, 118, 255)'
                ))

fig.update_layout(
    title='Aferição do tempo de Carga',
    xaxis_tickfont_size=14,
    legend=dict(
        x=0,
        y=1.0,
        bgcolor='rgba(255, 255, 255, 0)',
        bordercolor='rgba(255, 255, 255, 0)'
    ),
    barmode='group',
    bargap=0.15, # gap between bars of adjacent location coordinates.
    bargroupgap=0.1 # gap between bars of the same location coordinate.
)
fig.show()

                     weight  load_time
date_created                          
2021-08-06 08:00:00  26.958  11.283333
2021-08-06 09:00:00  31.159  12.683333
2021-08-06 10:00:00  21.884   9.250000
2021-08-06 11:00:00  10.305   4.416667
2021-08-06 12:00:00  10.583   5.183333
...                     ...        ...
2021-08-31 13:00:00  24.425  10.216667
2021-08-31 15:00:00  12.790   5.500000
2021-08-31 16:00:00  27.679  11.483333
2021-08-31 17:00:00  22.011   9.416667
2021-08-31 18:00:00  21.070   8.433333

[162 rows x 2 columns]

Consumo de Combustível da Escavadeira

O Consumo de Combustível é o cálculo do volume consumido em relação a um período de tempo.

Forma de Cálculo: Média de consumo de combustível no dia

• Cc = V combustível / T operação (L/dia ou L/h)

df_comb = df_total.copy()
df_max = df_comb.groupby(pd.Grouper(freq='86400s')).max().fillna(0)
df_min = df_comb.groupby(pd.Grouper(freq='86400s')).min().fillna(0)
df_comb = df_comb.groupby(pd.Grouper(freq='86400s')).max().fillna(0)
diff_fuel = df_max['fuel_esc'] - df_min['fuel_esc']
df_comb['diff_fuel'] = diff_fuel
df_comb['consumo'] = (diff_fuel*litragem_esc)/100
# print(df_comb.loc[:, ['diff_fuel', 'consumo']])

fig = go.Figure()
fig.add_trace(go.Bar(x=df_comb.index,
                y=df_comb['diff_fuel'],
                name='Consumo do Combustivel em (%)',
                marker_color='rgb(55, 83, 109)'
                ))
fig.add_trace(go.Bar(x=df_comb.index,
                y=df_comb['consumo'],
                name='Consumo em Litros (L)',
                marker_color='rgb(26, 118, 255)'
                ))

fig.update_layout(
    title='Consumo de Combustível da Escavadeira',
    xaxis_tickfont_size=14,
    legend=dict(
        x=0,
        y=1.0,
        bgcolor='rgba(255, 255, 255, 0)',
        bordercolor='rgba(255, 255, 255, 0)'
    ),
    barmode='group',
    bargap=0.15, # gap between bars of adjacent location coordinates.
    bargroupgap=0.1 # gap between bars of the same location coordinate.
)
fig.show()

Consumo de Combustível do Caminhão

Dados do Caminhão:

• Modelo: Mercedes 2423k (mb 2423)
• Bateria (qtde x Ah/V): 2 x 135/12
• Volume Caçamba: 12 m³
• Velocidade Máxima: 82 Km/h
• Capacidade Subida (em mov.): 55% * 23t
• Capacidade Subida (parado): 39% * 23t
• Carga Máxima útil: 16,050 t
• Capacidade Combustível: 210 L
• Consumo Combustível: 1,5 Km/L a 3 Km/L

# df_data = df.loc[(df['imei'] == caminhao)]
df_data = df.copy()
df_data.loc[:,'date_created'] = pd.to_datetime(df_data['date_created'])
df_data = df_data.set_index('date_created')
df_data = df_data.loc['2021-08-06':]

df_comb = df_data.copy()
litragem = 300
df_max = df_comb.groupby(pd.Grouper(freq='86400s')).max().fillna(0)
df_min = df_comb.groupby(pd.Grouper(freq='86400s')).min().fillna(0)
df_comb = df_comb.groupby(pd.Grouper(freq='86400s')).max().fillna(0)
diff_fuel = df_max['fuel'] - df_min['fuel']
df_comb['diff_fuel'] = diff_fuel
df_comb['consumo'] = (diff_fuel*litragem)/100
# print(df_comb.loc[:, ['diff_fuel', 'consumo']])

fig = go.Figure()
fig.add_trace(go.Bar(x=df_comb.index,
                y=df_comb['diff_fuel'],
                name='Consumo de Combustivel (%)',
                marker_color='rgb(55, 83, 109)'
                ))
fig.add_trace(go.Bar(x=df_comb.index,
                y=df_comb['consumo'],
                name='Consumo em Litros (L)',
                marker_color='rgb(26, 118, 255)'
                ))

fig.update_layout(
    title='Consumo de Combustível do Caminhão',
    xaxis_tickfont_size=14,
    legend=dict(
        x=0,
        y=1.0,
        bgcolor='rgba(255, 255, 255, 0)',
        bordercolor='rgba(255, 255, 255, 0)'
    ),
    barmode='group',
    bargap=0.15, # gap between bars of adjacent location coordinates.
    bargroupgap=0.1 # gap between bars of the same location coordinate.
)
fig.show()

Peso Transportado por Combustível Consumido

A relação entre os dois dados permitirá a verificação da eficácia de maquinários nas realizações das operações elencadas.

• Forma de Cálculo: Peso Transportado por um equipamento em relação à quantidade de combustível consumido em determinado trecho
• Unidade: Kg/L
• PtCC = Massa Transportada (Kg) / Volume Consumido (L)

# df_transp = df.copy()
df_transp = df.loc[df['date_created'] < '2021-08-07 00:00:00']
# display(df_transp)
last_status = df_transp.iloc[0,1]
weight_acc = 0 #Peso acumulado

def WeightPerFuel(df):
    global last_status, weight_acc
    weight_per_fuel = None
    cur_status = df['status']
    if (last_status == 'Parado' or last_status == 'Movimento' or last_status == 'Descarregando') and (cur_status == 'Carregando'):
        weight_acc += df['weight']
    if (cur_status == 'Movimento') and (last_status == 'Carregando'):
        weight_per_fuel = round(weight_acc/df['fuel_comsumed'],2)
        weight_acc = 0
    last_status = cur_status
    return weight_per_fuel

df_transp['W/F'] = df_transp.apply(lambda x: WeightPerFuel(x), axis=1)
df_transp = df_transp.dropna(axis=0)
# display(df_transp) 
fig = px.line(df_transp, x="date_created", y="W/F", title="Peso Transportado por Combustível Comsumido (Kg/L)")
fig.show()

Trajeto Ciclo de Carga

# Plota Gráfico
def plotDatas(fig, datas, is_line=True, label=''):
        if fig is None:
            fig = go.Figure(go.Scattermapbox(
            mode = "markers+lines" if is_line == True else "markers",
            lon = datas[:,1],
            lat = datas[:,0],
            text = label if label != '' else 'veic1',
            marker = {'size': 10,
                    'color': 'blue'}))
        else:
            fig.add_trace(go.Scattermapbox(
            mode = "markers+lines" if is_line == True else "markers",
            lon = datas[:,1],
            lat = datas[:,0],
            text = label if label != '' else 'veic2',
            marker = {'size': 10,
                    'color': 'red'}))    

        fig.update_layout(
            margin ={'l':0,'t':0,'b':0,'r':0},
            #'center': {'lon': -38.71901, 'lat':  -3.94467},
            mapbox = {
                'center': {'lon': datas[:,1].mean(), 'lat':  datas[:,0].mean()},
                'style': "stamen-terrain",
                'center': {'lon': datas[:,1].mean(), 'lat':  datas[:,0].mean()},
                'zoom': 16})
        return fig

Geração de Dados

Trajeto do Caminhão dos dias 05/08 a 18/08

# import gmaps
from datetime import datetime
df_interval = df.loc[(df['imei'] == caminhao)]
df_gps = df.loc[:, ['latitude','longitude']]
gps_tuples = [tuple(x) for x in df_gps.to_numpy()]
start = gps_tuples[0]
end = gps_tuples[1]
# api_key_google = open('../api-google.txt').read()
# gmaps.configure(api_key=api_key_google)
# fig = gmaps.figure()
# layer = gmaps.directions.Directions(start, end,waypoints=gps_tuples)
# markers = gmaps.marker_layer(gps_tuples)
# layer = gmaps.symbol_layer(df_gps, fill_color = 'red', stroke_color='red', scale=2)
# fig.add_layer(layer)
# fig
def plotDataTimestamp(df):
    subset = df.loc[:, ['date_created','velocity','fuel']]
    text_df = [tuple(x) for x in subset.to_numpy()]
    fig = go.Figure(go.Scattermapbox(
        lat = df['latitude'].to_numpy(),
        lon = df['longitude'].to_numpy(),
        mode = 'markers',
        marker = go.scattermapbox.Marker( size = 9),
        # text = df['date_created'].astype('string').to_numpy()
        text = text_df
    ))

    mapbox_access_token = open('../mapbox_token.txt').read()

    fig.update_layout(
        margin ={'l':0,'t':0,'b':0,'r':0},
        #'center': {'lon': -38.71901, 'lat':  -3.94467},
        mapbox = {
            'center': {'lon': df['longitude'].to_numpy()[0], 'lat':  df['latitude'].to_numpy()[0]},
            'style': "stamen-terrain",
            'center': {'lon': df['longitude'].to_numpy()[0], 'lat':  df['latitude'].to_numpy()[0]},
            'zoom': 16})

    fig.show()


plotDataTimestamp(df_interval)

Trajeto de Abastecimento

from geopy.distance import great_circle

df_abast = df.copy()
df_abast = df_abast.loc[(df['imei'] == caminhao) & 
                        (df['date_created'] >= '2021-08-07 12:29:18') &
                        (df['date_created'] <= '2021-08-07 12:41:47')]


timestamp = df_abast['date_created'].to_numpy()
interval = float((timestamp[-1] - timestamp[0]) / (10**9))
location = df_abast.loc[:,['latitude','longitude']].to_numpy()
distance = great_circle(location[0], location[-1]).m
vel_media = (distance*3600)/(1000*interval)
print(f'Total Distance: {round(distance, 2)} m, Interval: {round((interval / 60), 2)} min, Vel Media: {round(vel_media, 2)} Km/h')
plotDataTimestamp(df_abast)

Total Distance: 3920.73 m, Interval: 12.48 min, Vel Media: 18.84 Km/h

Trajeto ao Ponto de Apoio

from geopy.distance import great_circle
from IPython.display import display, HTML
import random

litragem_cam = 210
df_apoio = df.copy()
df_apoio = df_apoio.loc[(df['imei'] == caminhao) & 
                        (df['date_created'] >= '2021-08-07 08:50:23') &
                        (df['date_created'] <= '2021-08-07 08:55:50')]


timestamp = df_apoio['date_created'].to_numpy()
interval = float((timestamp[-1] - timestamp[0]) / (10**9))
location = df_apoio.loc[:,['latitude','longitude']].to_numpy()
distance = great_circle(location[0], location[-1]).m
vel_media = (distance*3600)/(1000*interval)
autonomia = (distance/1000)/(0.71*litragem_cam/100)
print(f'Total Distance: {round(distance, 2)} m, Interval: {round((interval / 60), 2)} min, \
        Vel Media: {round(vel_media, 2)} Km/h, Autonomia: {round(autonomia, 2)} Km/L, max: {int(df["fuel"].max())}, min: {int(df["fuel"].min())}')

last_lat = df_apoio['latitude'].to_numpy()[0]
last_lon = df_apoio['longitude'].to_numpy()[0]

def calcDistance(df):
        global last_lat, last_lon
        dist = great_circle([df['latitude'],df['longitude']], [last_lat, last_lon]).m
        last_lat = df['latitude']
        last_lon = df['longitude']
        return dist

df_apoio['distance'] = df_apoio.apply(lambda x: calcDistance(x), axis=1)

nivel_comb = df_apoio['fuel'].to_numpy()[0]*litragem_cam/100
def calcLevel(df):
        global nivel_comb
        autonomia = random.uniform(1.6, 2.4)
        nivel_calc = (nivel_comb - ((df['distance']/1000)/autonomia))
        nivel_comb = nivel_calc
        return nivel_calc


df_apoio['nivel_litros'] = df_apoio.apply(lambda x: calcLevel(x), axis=1)
df_apoio['fuel_est'] = df_apoio.apply(lambda x: int(x['nivel_litros']*100/litragem_cam), axis=1)
display(df_apoio.head())
plotDataTimestamp(df_apoio)

Total Distance: 3281.38 m, Interval: 5.45 min,         Vel Media: 36.13 Km/h, Autonomia: 2.2 Km/L, max: 114, min: 5

	latitude	longitude	status	angle	fuel	weight	velocity	imei	date_created	distance	nivel_litros	fuel_est
6622	-3.879148	-38.761066	Parado	2.375795	96.0	-1.0	0.747417	25	2021-08-07 08:50:23	0.000000	201.600000	96
6623	-3.879255	-38.761116	Movimento	-7.255507	95.0	-1.0	16.074614	25	2021-08-07 08:50:28	13.127406	201.592838	95
6624	-3.880620	-38.761467	Movimento	6.199142	92.0	-1.0	47.606140	25	2021-08-07 08:50:54	156.696793	201.503250	95
6625	-3.883064	-38.761730	Parado	6.118005	92.0	-1.0	0.444744	25	2021-08-07 08:51:25	273.322582	201.337315	95
6626	-3.883070	-38.761730	Parado	6.175217	93.0	-1.0	0.245021	25	2021-08-07 08:51:31	0.667171	201.336997	95

Trajeto ao Ponto de Carregamento

Método de Geração: Através dos dados reais de geolocalização é estimado o consumo de combustível do caminhão em Km,L.

from geopy.distance import great_circle
from IPython.display import display, HTML
import random

class circleWay():
        def __init__(self, df):
                circleWay.generate(df)
        
        @classmethod
        def generate(cls, df):
                litragem_cam = 210
                df_apoio = df.copy()
                df_apoio = df_apoio.loc[(df['imei'] == caminhao) & 
                                        (df['date_created'] >= '2021-08-06 16:16:39') &
                                        (df['date_created'] <= '2021-08-06 16:32:09')]


                timestamp = df_apoio['date_created'].to_numpy()
                interval = float((timestamp[-1] - timestamp[0]) / (10**9))
                location = df_apoio.loc[:,['latitude','longitude']].to_numpy()
                distance = great_circle(location[0], location[-1]).m
                vel_media = (distance*3600)/(1000*interval)
                autonomia = (distance/1000)/(0.71*litragem_cam/100)
                print(f'Total Distance: {round(distance, 2)} m, Interval: {round((interval / 60), 2)} min, \
                        Vel Media: {round(vel_media, 2)} Km/h, Autonomia: {round(autonomia, 2)} Km/L, max: {int(df["fuel"].max())}, min: {int(df["fuel"].min())}')

                cls.last_lat = df_apoio['latitude'].to_numpy()[0]
                cls.last_lon = df_apoio['longitude'].to_numpy()[0]

                df_apoio['distance'] = df_apoio.apply(lambda x: circleWay.calcDistance(x), axis=1)

                cls.nivel_comb = df_apoio['fuel'].to_numpy()[0]*litragem_cam/100

                df_apoio['nivel_litros'] = df_apoio.apply(lambda x: calcLevel(x), axis=1)
                df_apoio['fuel_est'] = df_apoio.apply(lambda x: int(x['nivel_litros']*100/litragem_cam), axis=1)
                print(f'Distance Calc: {df_apoio["distance"].sum()}')
                return df_apoio
        
        @classmethod
        def calcDistance(cls, df):
                dist = great_circle([df['latitude'],df['longitude']], [cls.last_lat, cls.last_lon]).m
                cls.last_lat = df['latitude']
                cls.last_lon = df['longitude']
                return dist

        @classmethod
        def calcLevel(cls, df):
                autonomia = random.uniform(1.6, 2.4)
                nivel_calc = (cls.nivel_comb - ((df['distance']/1000)/autonomia))
                cls.nivel_comb = nivel_calc
                return nivel_calc

df_way = circleWay.generate(df)
display(df_way.head())
plotDataTimestamp(df_way)

Total Distance: 550.43 m, Interval: 15.5 min,                         Vel Media: 2.13 Km/h, Autonomia: 0.37 Km/L, max: 114, min: 5
Distance Calc: 817.2126977863818

	latitude	longitude	status	angle	fuel	weight	velocity	imei	date_created	distance	nivel_litros	fuel_est
3868	-3.909287	-38.758678	Movimento	6.057806	80.0	-1.0	16.226978	25	2021-08-06 16:16:39	0.000000	166.774652	79
3870	-3.909756	-38.759205	Movimento	-3.758087	81.0	-1.0	29.515764	25	2021-08-06 16:16:49	78.343146	166.735454	79
3872	-3.909950	-38.759506	Movimento	-10.048460	82.0	-1.0	30.370251	25	2021-08-06 16:16:54	39.753721	166.715680	79
3874	-3.910157	-38.759769	Movimento	3.384842	83.0	-1.0	26.639341	25	2021-08-06 16:17:00	37.162516	166.696213	79
3876	-3.910119	-38.760246	Movimento	1.779030	81.0	-1.0	27.506182	25	2021-08-06 16:17:10	53.085022	166.672555	79

Calcula nova posição a partir da posição atual e um determinado deslocamento angular:

• Parâmetros:
  • location: lista contendo a geolocalização
  • dist: distancia em metros em relação ao ponto
• Retorno:
  • Lista contendo a nova geolocalização decimal

from geopy.distance import distance, great_circle
import random 

start_point = (-3.910157, -38.759769)
'''
    Retorna novo ponto com distancia 'dist' em metros em relação ao primeiro ponto 'location'
'''
def genNewLocation(location, dist):
    degress = [0,90,180,270]
    idx = random.randint(0,3)
    new_point = distance(meters=dist).destination(location, bearing=degress[idx])
    new_point_d = new_point.format_decimal() 
    new_point_f = [round(float(x), 6) for x in list(new_point_d.split(','))]
    return new_point_f
    
new_point = genNewLocation(start_point, 15)    
print(new_point)
print(great_circle((-3.910157, -38.759769), new_point).m)

[-3.910157, -38.759904]
14.976393003006738

Geração de Dados de Carregamento

Método de geração: Estima-se a quantidade de carregamentos realizados pela escavadeira com base no volume da caçamba do caminhão e na capacidade da concha da escavadeira. Os dados de combustível são gerados a partir da autonomia, em L/h, da escavadeira. O tempo de carregamento é calculado através do intervalo estimado de cada 'conchada'.

Dados da Escavadeira:

• Modelo: Cat320D
  • Bateria
  • Capacidade Combustível: 345 L
  • Capascidade da Caçamba: 732 Kg (1,19 m³)
  • Autonomia: 9 L/h a 12 L/h
  • Tempo de Carga de um Caminhao: 4min a 8min

import random
from datetime import datetime, timedelta

start_point = (-3.908756, -38.763611)
start_time = '2021-08-07 14:00:00'
start_time_date = datetime.fromisoformat(start_time)
print(start_time_date)
litragem_esc = 345
start_fuel = fuel = 345
weight_sum = 0
weights = []
lats = []
longs = []
dates =[]
intervals = [] #intervalo em segundos entre as conchadas
fuels = [] #combustivel em litros da escavadeira
total_weight = random.randint(9470, 14350) # peso estimado suportado pela caçamba do caminhão.


while weight_sum < total_weight:
    w = random.randint(480,640) # peso estimado coletado pela concha da escavadeira.
    weight_sum += w
    weights.append(w)
    interval = random.randint(12,19)
    intervals.append(interval) # tempo considerado para coletar uma amostra e depositá-la no caminhão.
    # Calculo baseado na autonomia da escavadeira que varia entre 9 a 12 L/h
    fuel -= random.randint(9, 12)*interval/3600 
    fuels.append(fuel)

for idx in range(len(weights)):
    # distancia em metros em relacao ao ponto geografico inicial. 
    # Isto simula o erro inerente ao gps, considerando o veículo no estado parado. 
    _dist = random.uniform(0.4,1.3) 
    new_point = genNewLocation(start_point, _dist)
    (lat, lon) = tuple(new_point)
    lats.append(lat)
    longs.append(lon)


df_dict = {
    'latitude': lats,
    'longitude': longs,
    'weight': weights,
    'interval': intervals,
    'fuel_levels':fuels
}   

last_time = start_time_date
def updateTime(df):
    global last_time
    curr_time = last_time + timedelta(seconds = df['interval'])
    last_time = curr_time
    return curr_time

velocities = [round(random.randint(0,0), 2) for _ in range(len(weights))]
df_esc = pd.DataFrame(df_dict)
# df_esc['fuel'] = df_esc['fuel_levels']*100/litragem_esc
df_esc['fuel'] = df_esc['fuel_levels'].apply(lambda x: int(x*100/litragem_esc))
df_esc['date_created'] = df_esc.apply(lambda x: updateTime(x), axis=1)
df_esc['velocity'] = pd.Series(velocities)
df_esc['status'] = 'Carregando'
print(f'Peso total: {df_esc["weight"].sum()} Kg, Tempo total: {round(df_esc["interval"].sum()/60, 2)} min, Total amostras: {df_esc["weight"].count()}')
display(df_esc)
plotDataTimestamp(df_esc)

2021-08-07 14:00:00
Peso total: 10328 Kg, Tempo total: 4.83 min, Total amostras: 19

	latitude	longitude	weight	interval	fuel_levels	fuel	date_created	velocity	status
0	-3.908756	-38.763602	560	14	344.957222	99	2021-08-07 14:00:14	0	Carregando
1	-3.908744	-38.763611	607	14	344.918333	99	2021-08-07 14:00:28	0	Carregando
2	-3.908766	-38.763611	516	12	344.888333	99	2021-08-07 14:00:40	0	Carregando
3	-3.908756	-38.763601	549	15	344.842500	99	2021-08-07 14:00:55	0	Carregando
4	-3.908756	-38.763618	503	14	344.803611	99	2021-08-07 14:01:09	0	Carregando
5	-3.908756	-38.763621	638	12	344.773611	99	2021-08-07 14:01:21	0	Carregando
6	-3.908751	-38.763611	522	15	344.723611	99	2021-08-07 14:01:36	0	Carregando
7	-3.908766	-38.763611	591	18	344.678611	99	2021-08-07 14:01:54	0	Carregando
8	-3.908756	-38.763600	509	19	344.620556	99	2021-08-07 14:02:13	0	Carregando
9	-3.908761	-38.763611	501	14	344.585556	99	2021-08-07 14:02:27	0	Carregando
10	-3.908756	-38.763615	544	15	344.539722	99	2021-08-07 14:02:42	0	Carregando
11	-3.908748	-38.763611	568	13	344.503611	99	2021-08-07 14:02:55	0	Carregando
12	-3.908747	-38.763611	519	15	344.461944	99	2021-08-07 14:03:10	0	Carregando
13	-3.908756	-38.763615	501	15	344.416111	99	2021-08-07 14:03:25	0	Carregando
14	-3.908762	-38.763611	606	18	344.366111	99	2021-08-07 14:03:43	0	Carregando
15	-3.908751	-38.763611	538	19	344.308056	99	2021-08-07 14:04:02	0	Carregando
16	-3.908756	-38.763618	483	16	344.263611	99	2021-08-07 14:04:18	0	Carregando
17	-3.908764	-38.763611	567	18	344.203611	99	2021-08-07 14:04:36	0	Carregando
18	-3.908765	-38.763611	506	14	344.164722	99	2021-08-07 14:04:50	0	Carregando

import random
import numpy as np
from datetime import datetime, timedelta
from geopy.distance import distance, great_circle

class circleLoading():
    def __init__(self, df_main):
        circleLoading.generate(df_main)

    @classmethod
    def generate(cls, df_main):
        cls.df_main = df_main
        last_row = df_main.tail(1)
        start_point = (last_row['latitude'].values[0], last_row['longitude'].values[0])
        # start_time = last_row['date_created'].values
        # start_time_date = pd.Timestamp(last_row['date_created'].values[0]).to_datetime64()
        start_time_date = last_row['date_created'].values[0]
        cls.last_time = pd.to_datetime(start_time_date)
        # print(last_time)
        litragem_esc = 345
        fuel = 345
        weight_sum = 0
        weights = []
        lats = []
        longs = []
        intervals = [] #intervalo em segundos entre as conchadas
        fuels = [] #combustivel em litros da escavadeira
        total_weight = random.randint(9470, 14350) # peso estimado suportado pela caçamba do caminhão.


        while weight_sum < total_weight:
            w = random.randint(480,640) # peso estimado coletado pela concha da escavadeira.
            weight_sum += w
            weights.append(w)
            interval = random.randint(12,19)
            intervals.append(interval) # tempo considerado para coletar uma amostra e depositá-la no caminhão.
            # Calculo baseado na autonomia da escavadeira que varia entre 9 a 12 L/h
            fuel -= random.randint(9, 12)*interval/3600 
            fuels.append(fuel)

        for _ in range(len(weights)):
            # distancia em metros em relacao ao ponto geografico inicial. 
            # Isto simula o erro inerente ao gps, considerando o veículo no estado parado. 
            _dist = random.uniform(0.4,1.3) 
            new_point = genNewLocation(start_point, _dist)
            (lat, lon) = tuple(new_point)
            lats.append(lat)
            longs.append(lon)


        df_dict = {
            'latitude': lats,
            'longitude': longs,
            'weight': weights,
            'interval': intervals,
            'fuel_levels':fuels
        }   

        velocities = [round(random.randint(0,0), 2) for _ in range(len(weights))]
        df_new = pd.DataFrame(df_dict)
        # df_esc['fuel'] = df_esc['fuel_levels']*100/litragem_esc
        df_new['fuel'] = df_new['fuel_levels'].apply(lambda x: int(x*100/litragem_esc))
        df_new['date_created'] = df_new.apply(lambda x: circleLoading.updateTime(x), axis=1)
        df_new['velocity'] = pd.Series(velocities)
        df_new['status'] = 'Carregando'
        print(f'Peso total: {df_new["weight"].sum()} Kg, Tempo total: {round(df_new["interval"].sum()/60, 2)} min, Total amostras: {df_new["weight"].count()}')
        # display(df_esc)
        # plotDataTimestamp(df_esc)
        return df_new
    
    @classmethod
    def updateTime(cls, df):
        # print(f'las_time: {cls.last_time}, type: {type(cls.last_time)}')
        curr_time = cls.last_time + np.timedelta64(int(df['interval']),'s')
        cls.last_time = curr_time
        return curr_time

    @classmethod
    def addOffsetLocation(cls, df):
        degress = [0,90,180,270]
        idx = random.randint(0,3)
        location = (df['latitude'],df['longitude'])
        new_point = distance(meters=3).destination(location, bearing=degress[idx])
        new_point_d = new_point.format_decimal() 
        new_point_f = [round(float(x), 6) for x in list(new_point_d.split(','))]
        return new_point_f

    @classmethod
    def convertToTruck(cls, df, fuel_init):
        res = df.apply(lambda x: circleLoading.addOffsetLocation(x), axis=1)
        df['latitude'] = res.apply(lambda x: x[0])
        df['longitude'] = res.apply(lambda x: x[0])
        df['weight'] = -1
        fuel_truck = []
        for _ in range(df['date_created'].count()):
            fuel_init -= random.randint(9, 12)*interval/3600 
            fuel_truck.append(fuel_init)
        df['fuel_levels'] = pd.Series(fuel_truck)
        # df['fuel'] = df['fuel_levels'].apply(lambda x: int(x*100/litragem_cam))
        display(df)


display(df_apoio.tail(1))
df_return = circleLoading.generate(df_apoio)
df_apoio = df_apoio.append(df_return, ignore_index=True)
display(df_return)
circleLoading.convertToTruck(df_return, fuel_init = 200)

	latitude	longitude	status	angle	fuel	weight	velocity	imei	date_created	distance	nivel_litros	fuel_est	interval	fuel_levels
1495	-3.908813	-38.76366	Carregando	NaN	99.0	629.0	0.0	NaN	2021-08-06 22:22:07	NaN	NaN	NaN	19.0	344.068889

Peso total: 12068 Kg, Tempo total: 5.58 min, Total amostras: 22

	latitude	longitude	weight	interval	fuel_levels	fuel	date_created	velocity	status
0	-3.908823	-38.763660	481	18	344.955000	99	2021-08-06 22:22:25	0	Carregando
1	-3.908813	-38.763665	501	15	344.909167	99	2021-08-06 22:22:40	0	Carregando
2	-3.908824	-38.763660	513	15	344.859167	99	2021-08-06 22:22:55	0	Carregando
3	-3.908804	-38.763660	617	14	344.816389	99	2021-08-06 22:23:09	0	Carregando
4	-3.908813	-38.763656	486	17	344.769167	99	2021-08-06 22:23:26	0	Carregando
5	-3.908813	-38.763669	527	18	344.709167	99	2021-08-06 22:23:44	0	Carregando
6	-3.908803	-38.763660	497	16	344.655833	99	2021-08-06 22:24:00	0	Carregando
7	-3.908802	-38.763660	580	17	344.608611	99	2021-08-06 22:24:17	0	Carregando
8	-3.908824	-38.763660	628	17	344.551944	99	2021-08-06 22:24:34	0	Carregando
9	-3.908821	-38.763660	600	13	344.515833	99	2021-08-06 22:24:47	0	Carregando
10	-3.908806	-38.763660	519	16	344.462500	99	2021-08-06 22:25:03	0	Carregando
11	-3.908813	-38.763664	501	12	344.425833	99	2021-08-06 22:25:15	0	Carregando
12	-3.908806	-38.763660	530	16	344.376944	99	2021-08-06 22:25:31	0	Carregando
13	-3.908807	-38.763660	600	14	344.330278	99	2021-08-06 22:25:45	0	Carregando
14	-3.908813	-38.763672	578	12	344.296944	99	2021-08-06 22:25:57	0	Carregando
15	-3.908813	-38.763656	503	13	344.253611	99	2021-08-06 22:26:10	0	Carregando
16	-3.908813	-38.763650	589	19	344.200833	99	2021-08-06 22:26:29	0	Carregando
17	-3.908813	-38.763669	601	18	344.140833	99	2021-08-06 22:26:47	0	Carregando
18	-3.908808	-38.763660	577	14	344.105833	99	2021-08-06 22:27:01	0	Carregando
19	-3.908809	-38.763660	510	17	344.053889	99	2021-08-06 22:27:18	0	Carregando
20	-3.908825	-38.763660	554	12	344.017222	99	2021-08-06 22:27:30	0	Carregando
21	-3.908813	-38.763654	576	12	343.980556	99	2021-08-06 22:27:42	0	Carregando

	latitude	longitude	weight	interval	fuel_levels	fuel	date_created	velocity	status
0	-3.908823	-3.908823	-1	18	197.158333	99	2021-08-06 22:22:25	0	Carregando
1	-3.908813	-3.908813	-1	15	194.316667	99	2021-08-06 22:22:40	0	Carregando
2	-3.908824	-3.908824	-1	15	191.733333	99	2021-08-06 22:22:55	0	Carregando
3	-3.908804	-3.908804	-1	14	188.633333	99	2021-08-06 22:23:09	0	Carregando
4	-3.908813	-3.908813	-1	17	185.791667	99	2021-08-06 22:23:26	0	Carregando
5	-3.908840	-3.908840	-1	18	182.950000	99	2021-08-06 22:23:44	0	Carregando
6	-3.908776	-3.908776	-1	16	179.850000	99	2021-08-06 22:24:00	0	Carregando
7	-3.908802	-3.908802	-1	17	177.266667	99	2021-08-06 22:24:17	0	Carregando
8	-3.908824	-3.908824	-1	17	174.166667	99	2021-08-06 22:24:34	0	Carregando
9	-3.908821	-3.908821	-1	13	171.066667	99	2021-08-06 22:24:47	0	Carregando
10	-3.908806	-3.908806	-1	16	168.741667	99	2021-08-06 22:25:03	0	Carregando
11	-3.908786	-3.908786	-1	12	166.416667	99	2021-08-06 22:25:15	0	Carregando
12	-3.908833	-3.908833	-1	16	163.575000	99	2021-08-06 22:25:31	0	Carregando
13	-3.908834	-3.908834	-1	14	160.475000	99	2021-08-06 22:25:45	0	Carregando
14	-3.908813	-3.908813	-1	12	157.633333	99	2021-08-06 22:25:57	0	Carregando
15	-3.908786	-3.908786	-1	13	155.050000	99	2021-08-06 22:26:10	0	Carregando
16	-3.908840	-3.908840	-1	19	152.725000	99	2021-08-06 22:26:29	0	Carregando
17	-3.908840	-3.908840	-1	18	149.625000	99	2021-08-06 22:26:47	0	Carregando
18	-3.908808	-3.908808	-1	14	146.525000	99	2021-08-06 22:27:01	0	Carregando
19	-3.908836	-3.908836	-1	17	143.683333	99	2021-08-06 22:27:18	0	Carregando
20	-3.908798	-3.908798	-1	12	141.358333	99	2021-08-06 22:27:30	0	Carregando
21	-3.908813	-3.908813	-1	12	138.516667	99	2021-08-06 22:27:42	0	Carregando

class genPath():
    def __init__(self) -> None:
        pass

    @classmethod
    def gen(cls, df, loaded=False):
        # loaded: se o caminhao estiver carregado adiciona fator multiplicador no consumo
        velocity = round(random.uniform(3,9),1) #em Km/h
        distance = round(random.uniform(0.8,1.6),2) #em Km
        consumption = round(random.uniform(1.5, 3),1)*1.15 if loaded == True else round(random.uniform(1.5, 3),1) #em Km/L do caminhao
        interval = round((distance/velocity)*60,2) #em min
        
        status = "Movimento"
        date_created = df['date_created'].tail(1).values[0] + np.timedelta64(int(interval),'m')

        # Variáveis Caminhao 
        fuel_consumed = round(distance/consumption,2)
        fuel_level = df['fuel_level'].tail(1).values[0] - fuel_consumed
        fuel = int(fuel_level*100/litragem_cam)

        # Variáveis Escavadeira 
        # Com base na autonomia média da escavadeira. Fator multiplicador pois ela se encontra parada enquanto o caminhao se locomove
        fuel_consumed_esc = (random.randint(9, 12)*interval/60)*0.91 
        fuel_level_esc = df['fuel_level_esc'].tail(1).values[0] - fuel_consumed_esc
        fuel_esc = int(fuel_level_esc*100/litragem_esc)
        weight = 0

        df_dict = {
            'date_created': [date_created],
            'status': [status],
            'fuel': [fuel],
            'weight': [weight],
            'distance': [distance], 
            'fuel_level': [fuel_level],
            'fuel_comsumed': [fuel_consumed], 
            'interval': [interval], 
            'fuel_esc': [fuel_esc],
            'fuel_level_esc': [fuel_level_esc],
            'fuel_comsumed_esc': [fuel_consumed_esc]
        }

        df_res = pd.DataFrame(df_dict)
        return df_res

class genLoading():
    def __init__(self) -> None:
        pass

    @classmethod
    def gen(cls, df):
        auton_cam = round(random.uniform(0.3, 5.2),1) #em L/h do caminhao no estado parado
        capacity_weight = random.randint(9470, 14350) # peso estimado suportado pela caçamba do caminhão
        last_date = df['date_created'].tail(1).values[0]
        last_fuel = df['fuel_level'].tail(1).values[0]
        last_fuel_esc = df['fuel_level_esc'].tail(1).values[0]
        payload_sum = 0

        # Arrays de dados
        status = []
        dates_created = []
        fuels = []
        weights = []
        fuel_levels = []
        fuel_comsumeds = []
        intervals = []
        fuel_escs = []
        fuel_level_escs = []
        fuel_comsumed_escs = []
        distance = []

        while payload_sum < capacity_weight:
            payload_esc = random.randint(480,640) # peso estimado coletado pela concha da escavadeira
            weights.append(payload_esc)
            payload_sum += payload_esc
            interval = random.randint(9,19) # tempo em segundos (s) considerado para coletar uma amostra e depositá-la no caminhão.
            intervals.append(interval/60)
            date_created = last_date + np.timedelta64(interval,'s')
            dates_created.append(date_created)
            last_date = date_created
            status.append("Carregando")
            distance.append(0)
            
            # Variáveis Caminhao 
            fuel_consumed = (auton_cam*interval)/3600
            fuel_level = last_fuel - fuel_consumed
            last_fuel = fuel_level
            fuel = int(fuel_level*100/litragem_cam)
            fuel_comsumeds.append(fuel_consumed)
            fuel_levels.append(fuel_level)
            fuels.append(fuel)


            # Variáveis Escavadeira 
            # Consumo calculado com base na autonomia média da escavadeira.
            fuel_consumed_esc = (random.randint(9, 12)*interval/3600)
            fuel_level_esc = last_fuel_esc - fuel_consumed_esc
            last_fuel_esc = fuel_level_esc
            fuel_esc = int(fuel_level_esc*100/litragem_esc)
            fuel_escs.append(fuel_esc)
            fuel_level_escs.append(last_fuel_esc)
            fuel_comsumed_escs.append(fuel_consumed_esc)


        df_dict = {
            'date_created': dates_created,
            'status': status,
            'fuel': fuels,
            'weight': weights,
            'distance': distance, 
            'fuel_level': fuel_levels,
            'fuel_comsumed': fuel_comsumeds, 
            'interval': intervals, 
            'fuel_esc': fuel_escs,
            'fuel_level_esc': fuel_level_escs,
            'fuel_comsumed_esc': fuel_comsumed_escs
        }

        df_res = pd.DataFrame(df_dict)
        return df_res

class genDischarging():
    def __init__(self) -> None:
        pass

    @classmethod
    def gen(cls, df):
        auton_cam = round(random.uniform(0.3, 5.2),1)*1.05 #em L/h do caminhao no estado parado, fator multiplicador considerando carga na caçamba
        distance = 0 #em Km
        interval = random.randint(3,6) #em min
        
        status = "Descarregando"
        date_created = df['date_created'].tail(1).values[0] + np.timedelta64(int(interval),'m')

        # Variáveis Caminhao 
        fuel_consumed = (auton_cam*interval)/60
        fuel_level = df['fuel_level'].tail(1).values[0] - fuel_consumed
        fuel = int(fuel_level*100/litragem_cam)

        # Variáveis Escavadeira 
        # Com base na autonomia média da escavadeira. Fator multiplicador pois ela se encontra parada enquanto o caminhao se locomove, 
        # logo seu consumo é menor em relação ao estado em que se encontra operando.
        fuel_consumed_esc = (random.randint(9, 12)*interval/60)*0.91 
        fuel_level_esc = df['fuel_level_esc'].tail(1).values[0] - fuel_consumed_esc
        fuel_esc = int(fuel_level_esc*100/litragem_esc)
        weight = 0

        df_dict = {
            'date_created': [date_created],
            'status': [status],
            'fuel': [fuel],
            'weight': [weight],
            'distance': [distance], 
            'fuel_level': [fuel_level],
            'fuel_comsumed': [fuel_consumed], 
            'interval': [interval], 
            'fuel_esc': [fuel_esc],
            'fuel_level_esc': [fuel_level_esc],
            'fuel_comsumed_esc': [fuel_consumed_esc]
        }

        df_res = pd.DataFrame(df_dict)
        return df_res

def genOneCycle(df_cycle):
    # Ciclo de Uma Operação
    df_cycle = df_cycle.append(genPath.gen(df_cycle), ignore_index=True) #Caminho até a escavadeira
    df_cycle = df_cycle.append(genLoading.gen(df_cycle), ignore_index=True) #Ciclo de Carregamento
    df_cycle = df_cycle.append(genPath.gen(df_cycle, loaded=True), ignore_index=True) #Caminho até o ponto de Descarregamento
    df_cycle = df_cycle.append(genDischarging.gen(df_cycle), ignore_index=True) #Ciclo de Descarregamento
    return df_cycle


################## Inicio #########################################
day_list = []
date = pd.to_datetime(f'2021-08-06 07:00:00') 
qtde_dias = 18 #Quantidade de dias úteis gerados
for idx in range(qtde_dias):
    minutes = int(random.uniform(-1, 1)*57)
    date += np.timedelta64(int(minutes*60),'s')
    day_list.append(str(date))
    date += np.timedelta64(1,'D') if date.weekday() < 4 else np.timedelta64(3,'D')


df_dict = {
            'date_created': [pd.to_datetime(day_list[0])],
            'status': ['Parado'],
            'fuel': [98],
            'weight': [0],
            'distance': [0], 
            'fuel_level': [190],
            'fuel_comsumed': [0], 
            'interval': [0],
            'fuel_esc': [95],
            'fuel_level_esc': [327],
            'fuel_comsumed_esc': [0]
        }


df_cycle = pd.DataFrame(df_dict)

#Ciclo de 'day_list' dias
for date in day_list:
    fuel_init = random.randint(86,97)
    fuel_level_init = (fuel_init*litragem_cam)/100
    fuel_esc_init = random.randint(88,95)
    fuel_level_esc_init = (fuel_esc_init*litragem_esc)/100

    day_init = {
        'date_created': pd.to_datetime(date),
        'status': 'Parado',
        'fuel': fuel_init,
        'weight': 0,
        'distance': 0, 
        'fuel_level': fuel_level_init,
        'fuel_comsumed': 0, 
        'interval': 0,
        'fuel_esc': fuel_esc_init,
        'fuel_level_esc': fuel_level_esc_init,
        'fuel_comsumed_esc': 0
    }

    df_cycle = df_cycle.append(day_init, ignore_index=True)
    print(f'Inicio do Turno: {df_cycle.tail(1)}')

    #Ciclo de um Dia
    for turno in range(2):
        #Ciclo de um Turno
        for _ in range(7):
            df_cycle = genOneCycle(df_cycle)

        display(df_cycle)
        print(f'Turno {turno+1}: time interval: {df_cycle.iloc[-1,0] - df_cycle.iloc[0,0]}, interval: {round(df_cycle["interval"].sum()/60,2)} h, total_weight: {df_cycle["weight"].sum()} Kg')
        
        #Intervalo de Almoço
        df_dict_interval = {
                'date_created': [df_cycle['date_created'].tail(1).values[0] + np.timedelta64(int(random.randint(57,83)),'m')],
                'status': ['Parado'],
                'fuel': [df_cycle['fuel'].tail(1).values[0]],
                'weight': [0],
                'distance': [0], 
                'fuel_level': [df_cycle['fuel_level'].tail(1).values[0]],
                'fuel_comsumed': [0], 
                'interval': [0],
                'fuel_esc': [df_cycle['fuel_esc'].tail(1).values[0]],
                'fuel_level_esc': [df_cycle['fuel_level_esc'].tail(1).values[0]],
                'fuel_comsumed_esc': [0]
            }
        df_interval = pd.DataFrame(df_dict_interval)
        df_cycle = df_cycle.append(df_interval, ignore_index=True)

df_cycle.to_csv(f"..{sep}dados_csv{sep}dados_sim_{random.randint(0,1000)}.csv")

Inicio do Turno:          date_created  status  fuel  weight  distance  fuel_level  \
1 2021-08-06 07:53:00  Parado    91       0         0       191.1   

   fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
1              0         0        93          320.85                  0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
179	2021-08-06 11:11:14	Carregando	86	559	0.00	181.866056	0.016333	0.233333	83	288.625884	0.038889
180	2021-08-06 11:11:26	Carregando	86	592	0.00	181.852056	0.014000	0.200000	83	288.595884	0.030000
181	2021-08-06 11:11:38	Carregando	86	581	0.00	181.838056	0.014000	0.200000	83	288.565884	0.030000
182	2021-08-06 11:20:38	Movimento	86	0	1.37	181.398056	0.440000	9.560000	83	287.115951	1.449933
183	2021-08-06 11:25:38	Descarregando	86	0	0.00	181.284306	0.113750	5.000000	83	286.433451	0.682500

184 rows × 11 columns

Turno 1: time interval: 0 days 03:32:38, interval: 3.66 h, total_weight: 90306 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
351	2021-08-06 16:11:24	Carregando	81	550	0.00	171.089611	0.008000	0.150000	72	250.575529	0.022500
352	2021-08-06 16:11:35	Carregando	81	593	0.00	171.079833	0.009778	0.183333	72	250.544974	0.030556
353	2021-08-06 16:11:54	Carregando	81	511	0.00	171.062944	0.016889	0.316667	72	250.486918	0.058056
354	2021-08-06 16:20:54	Movimento	81	0	1.11	170.462944	0.600000	9.650000	72	248.876977	1.609942
355	2021-08-06 16:25:54	Descarregando	81	0	0.00	170.112944	0.350000	5.000000	71	247.966977	0.910000

356 rows × 11 columns

Turno 2: time interval: 0 days 08:32:54, interval: 7.76 h, total_weight: 172933 Kg
Inicio do Turno:            date_created  status  fuel  weight  distance  fuel_level  \
357 2021-08-09 07:10:00  Parado    97       0       0.0       203.7   

     fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
357            0.0       0.0        89          307.05                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
523	2021-08-09 11:08:30	Carregando	92	556	0.00	193.972750	0.016250	0.250000	77	268.281392	0.045833
524	2021-08-09 11:08:45	Carregando	92	603	0.00	193.956500	0.016250	0.250000	77	268.235558	0.045833
525	2021-08-09 11:08:57	Carregando	92	550	0.00	193.943500	0.013000	0.200000	77	268.205558	0.030000
526	2021-08-09 11:20:57	Movimento	92	0	1.12	193.293500	0.650000	12.680000	77	265.897798	2.307760
527	2021-08-09 11:24:57	Descarregando	91	0	0.00	192.957500	0.336000	4.000000	76	265.169798	0.728000

528 rows × 11 columns

Turno 1: time interval: 3 days 03:31:57, interval: 12.1 h, total_weight: 256222 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
702	2021-08-09 16:56:56	Carregando	86	615	0.00	182.579278	0.006222	0.266667	64	221.589949	0.044444
703	2021-08-09 16:57:07	Carregando	86	550	0.00	182.575000	0.004278	0.183333	64	221.553282	0.036667
704	2021-08-09 16:57:19	Carregando	86	597	0.00	182.570333	0.004667	0.200000	64	221.523282	0.030000
705	2021-08-09 17:16:19	Movimento	86	0	1.16	182.090333	0.480000	19.890000	63	217.903302	3.619980
706	2021-08-09 17:19:19	Descarregando	86	0	0.00	182.053583	0.036750	3.000000	63	217.493802	0.409500

707 rows × 11 columns

Turno 2: time interval: 3 days 09:26:19, interval: 16.84 h, total_weight: 343243 Kg
Inicio do Turno:            date_created  status  fuel  weight  distance  fuel_level  \
708 2021-08-10 07:37:00  Parado    90       0       0.0       189.0   

     fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
708            0.0       0.0        90           310.5                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
879	2021-08-10 11:41:32	Carregando	85	498	0.00	178.811417	0.004500	0.300000	78	269.845945	0.050000
880	2021-08-10 11:41:49	Carregando	85	495	0.00	178.807167	0.004250	0.283333	78	269.794001	0.051944
881	2021-08-10 11:42:06	Carregando	85	550	0.00	178.802917	0.004250	0.283333	78	269.737334	0.056667
882	2021-08-10 11:49:06	Movimento	85	0	0.95	178.522917	0.280000	7.310000	77	268.517782	1.219552
883	2021-08-10 11:54:06	Descarregando	84	0	0.00	178.076667	0.446250	5.000000	77	267.683616	0.834167

884 rows × 11 columns

Turno 1: time interval: 4 days 04:01:06, interval: 21.23 h, total_weight: 429449 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
1051	2021-08-10 16:17:01	Carregando	80	619	0.00	168.457278	0.013000	0.300000	67	234.165503	0.055000
1052	2021-08-10 16:17:10	Carregando	80	518	0.00	168.450778	0.006500	0.150000	67	234.140503	0.025000
1053	2021-08-10 16:17:27	Carregando	80	586	0.00	168.438500	0.012278	0.283333	67	234.088559	0.051944
1054	2021-08-10 16:28:27	Movimento	79	0	1.44	167.808500	0.630000	11.080000	67	232.071999	2.016560
1055	2021-08-10 16:33:27	Descarregando	79	0	0.00	167.756000	0.052500	5.000000	67	231.389499	0.682500

1056 rows × 11 columns

Turno 2: time interval: 4 days 08:40:27, interval: 25.03 h, total_weight: 513755 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
1057 2021-08-11 07:30:00  Parado    86       0       0.0       180.6   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
1057            0.0       0.0        94           324.3                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
1223	2021-08-11 11:29:21	Carregando	81	559	0.00	171.314861	0.005444	0.233333	82	284.601265	0.035000
1224	2021-08-11 11:29:33	Carregando	81	483	0.00	171.310194	0.004667	0.200000	82	284.561265	0.040000
1225	2021-08-11 11:29:42	Carregando	81	523	0.00	171.306694	0.003500	0.150000	82	284.536265	0.025000
1226	2021-08-11 11:36:42	Movimento	81	0	1.02	170.866694	0.440000	7.650000	82	283.376015	1.160250
1227	2021-08-11 11:40:42	Descarregando	81	0	0.00	170.803694	0.063000	4.000000	81	282.648015	0.728000

1228 rows × 11 columns

Turno 1: time interval: 5 days 03:47:42, interval: 29.3 h, total_weight: 596907 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
1399	2021-08-11 16:26:38	Carregando	76	482	0.00	161.088500	0.008500	0.283333	72	248.710738	0.056667
1400	2021-08-11 16:26:48	Carregando	76	575	0.00	161.083500	0.005000	0.166667	72	248.680183	0.030556
1401	2021-08-11 16:26:57	Carregando	76	578	0.00	161.079000	0.004500	0.150000	72	248.652683	0.027500
1402	2021-08-11 16:38:57	Movimento	76	0	1.12	160.689000	0.390000	12.920000	71	246.889103	1.763580
1403	2021-08-11 16:42:57	Descarregando	76	0	0.00	160.395000	0.294000	4.000000	71	246.282436	0.606667

1404 rows × 11 columns

Turno 2: time interval: 5 days 08:49:57, interval: 33.13 h, total_weight: 682847 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
1405 2021-08-12 06:40:00  Parado    89       0       0.0       186.9   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
1405            0.0       0.0        95          327.75                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
1582	2021-08-12 10:26:41	Carregando	84	551	0.00	177.562583	0.001750	0.150000	84	290.535079	0.027500
1583	2021-08-12 10:26:57	Carregando	84	493	0.00	177.559472	0.003111	0.266667	84	290.490634	0.044444
1584	2021-08-12 10:27:16	Carregando	84	617	0.00	177.555778	0.003694	0.316667	84	290.437857	0.052778
1585	2021-08-12 10:35:16	Movimento	84	0	1.24	177.045778	0.510000	8.360000	83	289.296717	1.141140
1586	2021-08-12 10:38:16	Descarregando	84	0	0.00	176.904028	0.141750	3.000000	83	288.796217	0.500500

1587 rows × 11 columns

Turno 1: time interval: 6 days 02:45:16, interval: 37.19 h, total_weight: 771991 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
1748	2021-08-12 15:00:43	Carregando	79	560	0.00	167.920278	0.013611	0.233333	74	257.940994	0.035000
1749	2021-08-12 15:00:57	Carregando	79	489	0.00	167.906667	0.013611	0.233333	74	257.902105	0.038889
1750	2021-08-12 15:01:09	Carregando	79	483	0.00	167.895000	0.011667	0.200000	74	257.862105	0.040000
1751	2021-08-12 15:09:09	Movimento	79	0	0.94	167.535000	0.360000	8.170000	74	256.375165	1.486940
1752	2021-08-12 15:13:09	Descarregando	79	0	0.00	167.409000	0.126000	4.000000	74	255.768498	0.606667

1753 rows × 11 columns

Turno 2: time interval: 6 days 07:20:09, interval: 40.63 h, total_weight: 851935 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
1754 2021-08-13 07:20:00  Parado    90       0       0.0       189.0   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
1754            0.0       0.0        89          307.05                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
1926	2021-08-13 10:57:49	Carregando	85	555	0.00	179.278250	0.003611	0.216667	78	269.195634	0.043333
1927	2021-08-13 10:58:04	Carregando	85	631	0.00	179.274083	0.004167	0.250000	78	269.145634	0.050000
1928	2021-08-13 10:58:15	Carregando	85	500	0.00	179.271028	0.003056	0.183333	78	269.108967	0.036667
1929	2021-08-13 11:07:15	Movimento	85	0	1.02	178.881028	0.390000	9.870000	77	267.312627	1.796340
1930	2021-08-13 11:13:15	Descarregando	85	0	0.00	178.744528	0.136500	6.000000	77	266.493627	0.819000

1931 rows × 11 columns

Turno 1: time interval: 7 days 03:20:15, interval: 44.67 h, total_weight: 940066 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
2101	2021-08-13 16:42:53	Carregando	80	590	0.00	168.577556	0.008972	0.316667	65	226.698551	0.052778
2102	2021-08-13 16:43:10	Carregando	80	575	0.00	168.569528	0.008028	0.283333	65	226.641884	0.056667
2103	2021-08-13 16:43:22	Carregando	80	543	0.00	168.563861	0.005667	0.200000	65	226.605218	0.036667
2104	2021-08-13 16:53:22	Movimento	79	0	1.56	167.883861	0.680000	10.520000	65	224.850131	1.755087
2105	2021-08-13 16:57:22	Descarregando	79	0	0.00	167.673861	0.210000	4.000000	65	224.304131	0.546000

2106 rows × 11 columns

Turno 2: time interval: 7 days 09:04:22, interval: 49.16 h, total_weight: 1025723 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
2107 2021-08-16 07:40:00  Parado    91       0       0.0       191.1   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
2107            0.0       0.0        95          327.75                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
2289	2021-08-16 11:43:48	Carregando	86	548	0.00	180.903389	0.009028	0.216667	83	286.608166	0.043333
2290	2021-08-16 11:44:02	Carregando	86	602	0.00	180.893667	0.009722	0.233333	83	286.569277	0.038889
2291	2021-08-16 11:44:14	Carregando	86	500	0.00	180.885333	0.008333	0.200000	83	286.529277	0.040000
2292	2021-08-16 11:55:14	Movimento	85	0	1.07	180.485333	0.400000	11.070000	82	285.018222	1.511055
2293	2021-08-16 11:58:14	Descarregando	85	0	0.00	180.354083	0.131250	3.000000	82	284.563222	0.455000

2294 rows × 11 columns

Turno 1: time interval: 10 days 04:05:14, interval: 53.56 h, total_weight: 1118224 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
2471	2021-08-16 17:24:24	Carregando	81	640	0.00	170.170778	0.015111	0.283333	70	243.802254	0.047222
2472	2021-08-16 17:24:36	Carregando	81	620	0.00	170.160111	0.010667	0.200000	70	243.765588	0.036667
2473	2021-08-16 17:24:54	Carregando	81	495	0.00	170.144111	0.016000	0.300000	70	243.710588	0.055000
2474	2021-08-16 17:50:54	Movimento	80	0	1.42	169.374111	0.770000	26.620000	69	238.865748	4.844840
2475	2021-08-16 17:54:54	Descarregando	80	0	0.00	169.213111	0.161000	4.000000	69	238.259081	0.606667

2476 rows × 11 columns

Turno 2: time interval: 10 days 10:01:54, interval: 58.28 h, total_weight: 1208489 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
2477 2021-08-17 07:21:00  Parado    88       0       0.0       184.8   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
2477            0.0       0.0        90           310.5                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
2641	2021-08-17 11:23:53	Carregando	83	636	0.00	174.704611	0.017000	0.300000	78	271.068159	0.045000
2642	2021-08-17 11:24:03	Carregando	83	544	0.00	174.695167	0.009444	0.166667	78	271.037604	0.030556
2643	2021-08-17 11:24:14	Carregando	83	507	0.00	174.684778	0.010389	0.183333	78	271.003993	0.033611
2644	2021-08-17 11:33:14	Movimento	83	0	1.03	174.324778	0.360000	9.660000	78	269.392383	1.611610
2645	2021-08-17 11:38:14	Descarregando	82	0	0.00	174.114778	0.210000	5.000000	77	268.558216	0.834167

2646 rows × 11 columns

Turno 1: time interval: 11 days 03:45:14, interval: 62.69 h, total_weight: 1291104 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
2825	2021-08-17 16:25:36	Carregando	77	519	0.00	163.485250	0.022667	0.283333	67	231.894786	0.051944
2826	2021-08-17 16:25:47	Carregando	77	628	0.00	163.470583	0.014667	0.183333	67	231.858119	0.036667
2827	2021-08-17 16:25:59	Carregando	77	598	0.00	163.454583	0.016000	0.200000	67	231.824786	0.033333
2828	2021-08-17 16:35:59	Movimento	77	0	1.00	163.074583	0.380000	10.170000	66	230.128091	1.696695
2829	2021-08-17 16:38:59	Descarregando	77	0	0.00	162.917083	0.157500	3.000000	66	229.673091	0.455000

2830 rows × 11 columns

Turno 2: time interval: 11 days 08:45:59, interval: 66.75 h, total_weight: 1382385 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
2831 2021-08-18 08:02:00  Parado    92       0       0.0       193.2   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
2831            0.0       0.0        94           324.3                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
3004	2021-08-18 11:39:33	Carregando	86	542	0.00	182.565833	0.012889	0.266667	83	289.291648	0.053333
3005	2021-08-18 11:39:45	Carregando	86	566	0.00	182.556167	0.009667	0.200000	83	289.251648	0.040000
3006	2021-08-18 11:40:00	Carregando	86	526	0.00	182.544083	0.012083	0.250000	83	289.209982	0.041667
3007	2021-08-18 12:02:00	Movimento	86	0	1.53	182.014083	0.530000	22.390000	82	285.814165	3.395817
3008	2021-08-18 12:05:00	Descarregando	86	0	0.00	181.741083	0.273000	3.000000	82	285.268165	0.546000

3009 rows × 11 columns

Turno 1: time interval: 12 days 04:12:00, interval: 70.87 h, total_weight: 1470295 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
3182	2021-08-18 16:37:09	Carregando	82	626	0.00	172.468889	0.002667	0.200000	72	250.660206	0.040000
3183	2021-08-18 16:37:21	Carregando	82	499	0.00	172.466222	0.002667	0.200000	72	250.623539	0.036667
3184	2021-08-18 16:37:35	Carregando	82	542	0.00	172.463111	0.003111	0.233333	72	250.576873	0.046667
3185	2021-08-18 16:52:35	Movimento	81	0	1.03	172.033111	0.430000	15.070000	71	248.062694	2.514178
3186	2021-08-18 16:58:35	Descarregando	81	0	0.00	171.665611	0.367500	6.000000	71	247.243694	0.819000

3187 rows × 11 columns

Turno 2: time interval: 12 days 09:05:35, interval: 74.73 h, total_weight: 1557878 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
3188 2021-08-19 07:08:00  Parado    94       0       0.0       197.4   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
3188            0.0       0.0        93          320.85                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
3363	2021-08-19 11:10:10	Carregando	89	609	0.00	187.462389	0.021500	0.300000	81	280.391884	0.055000
3364	2021-08-19 11:10:21	Carregando	89	632	0.00	187.449250	0.013139	0.183333	81	280.364384	0.027500
3365	2021-08-19 11:10:35	Carregando	89	563	0.00	187.432528	0.016722	0.233333	81	280.317718	0.046667
3366	2021-08-19 11:20:35	Movimento	88	0	1.40	186.712528	0.720000	10.000000	80	278.497718	1.820000
3367	2021-08-19 11:23:35	Descarregando	88	0	0.00	186.539278	0.173250	3.000000	80	277.951718	0.546000

3368 rows × 11 columns

Turno 1: time interval: 13 days 03:30:35, interval: 79.12 h, total_weight: 1646670 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
3539	2021-08-19 16:41:59	Carregando	83	490	0.00	176.022556	0.002528	0.216667	68	236.764998	0.039722
3540	2021-08-19 16:42:13	Carregando	83	532	0.00	176.019833	0.002722	0.233333	68	236.718331	0.046667
3541	2021-08-19 16:42:25	Carregando	83	513	0.00	176.017500	0.002333	0.200000	68	236.688331	0.030000
3542	2021-08-19 16:54:25	Movimento	83	0	1.60	175.287500	0.730000	12.310000	68	235.008016	1.680315
3543	2021-08-19 16:59:25	Descarregando	83	0	0.00	175.226250	0.061250	5.000000	67	234.325516	0.682500

3544 rows × 11 columns

Turno 2: time interval: 13 days 09:06:25, interval: 83.56 h, total_weight: 1733255 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
3545 2021-08-20 07:53:00  Parado    95       0       0.0       199.5   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
3545            0.0       0.0        95          327.75                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
3713	2021-08-20 11:50:52	Carregando	89	518	0.00	188.852139	0.009750	0.216667	83	287.232132	0.039722
3714	2021-08-20 11:51:05	Carregando	89	637	0.00	188.842389	0.009750	0.216667	83	287.196021	0.036111
3715	2021-08-20 11:51:16	Carregando	89	520	0.00	188.834139	0.008250	0.183333	83	287.162410	0.033611
3716	2021-08-20 12:02:16	Movimento	89	0	1.38	188.314139	0.520000	11.500000	82	285.243827	1.918583
3717	2021-08-20 12:07:16	Descarregando	89	0	0.00	188.261639	0.052500	5.000000	82	284.561327	0.682500

3718 rows × 11 columns

Turno 1: time interval: 14 days 04:14:16, interval: 87.9 h, total_weight: 1817694 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
3879	2021-08-20 16:46:54	Carregando	85	491	0.00	178.761139	0.010833	0.166667	72	248.763988	0.025000
3880	2021-08-20 16:47:09	Carregando	85	626	0.00	178.744889	0.016250	0.250000	72	248.718154	0.045833
3881	2021-08-20 16:47:19	Carregando	85	495	0.00	178.734056	0.010833	0.166667	72	248.684821	0.033333
3882	2021-08-20 16:56:19	Movimento	84	0	1.30	178.144056	0.590000	9.510000	71	247.386706	1.298115
3883	2021-08-20 16:59:19	Descarregando	84	0	0.00	178.028556	0.115500	3.000000	71	246.931706	0.455000

3884 rows × 11 columns

Turno 2: time interval: 14 days 09:06:19, interval: 91.85 h, total_weight: 1898072 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
3885 2021-08-23 07:40:00  Parado    89       0       0.0       186.9   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
3885            0.0       0.0        92           317.4                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
4062	2021-08-23 11:44:36	Carregando	83	573	0.00	176.165222	0.017472	0.283333	80	277.154715	0.047222
4063	2021-08-23 11:44:48	Carregando	83	508	0.00	176.152889	0.012333	0.200000	80	277.124715	0.030000
4064	2021-08-23 11:45:02	Carregando	83	557	0.00	176.138500	0.014389	0.233333	80	277.085826	0.038889
4065	2021-08-23 11:52:02	Movimento	83	0	0.84	175.788500	0.350000	7.100000	80	276.116676	0.969150
4066	2021-08-23 11:55:02	Descarregando	83	0	0.00	175.631000	0.157500	3.000000	79	275.616176	0.500500

4067 rows × 11 columns

Turno 1: time interval: 17 days 04:02:02, interval: 96.23 h, total_weight: 1987502 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
4239	2021-08-23 16:58:29	Carregando	78	480	0.00	165.193083	0.002222	0.166667	69	238.120614	0.033333
4240	2021-08-23 16:58:45	Carregando	78	589	0.00	165.189528	0.003556	0.266667	69	238.076169	0.044444
4241	2021-08-23 16:58:58	Carregando	78	586	0.00	165.186639	0.002889	0.216667	68	238.043669	0.032500
4242	2021-08-23 17:06:58	Movimento	78	0	0.88	164.926639	0.260000	8.800000	68	236.575536	1.468133
4243	2021-08-23 17:10:58	Descarregando	78	0	0.00	164.646639	0.280000	4.000000	68	235.968869	0.606667

4244 rows × 11 columns

Turno 2: time interval: 17 days 09:17:58, interval: 100.41 h, total_weight: 2073461 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
4245 2021-08-24 07:53:00  Parado    88       0       0.0       184.8   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
4245            0.0       0.0        94           324.3                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
4408	2021-08-24 12:08:54	Carregando	82	554	0.00	173.420083	0.014167	0.166667	81	279.730582	0.027778
4409	2021-08-24 12:09:06	Carregando	82	628	0.00	173.403083	0.017000	0.200000	81	279.690582	0.040000
4410	2021-08-24 12:09:17	Carregando	82	503	0.00	173.387500	0.015583	0.183333	81	279.653915	0.036667
4411	2021-08-24 12:18:17	Movimento	82	0	0.91	173.117500	0.270000	9.930000	80	278.147865	1.506050
4412	2021-08-24 12:22:17	Descarregando	82	0	0.00	172.949500	0.168000	4.000000	80	277.541198	0.606667

4413 rows × 11 columns

Turno 1: time interval: 18 days 04:29:17, interval: 105.02 h, total_weight: 2155804 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
4585	2021-08-24 17:37:23	Carregando	77	632	0.00	162.839000	0.004333	0.200000	68	236.317498	0.040000
4586	2021-08-24 17:37:35	Carregando	77	596	0.00	162.834667	0.004333	0.200000	68	236.284165	0.033333
4587	2021-08-24 17:37:53	Carregando	77	484	0.00	162.828167	0.006500	0.300000	68	236.224165	0.060000
4588	2021-08-24 17:57:53	Movimento	77	0	1.37	162.168167	0.660000	20.050000	67	233.487340	2.736825
4589	2021-08-24 18:02:53	Descarregando	77	0	0.00	161.783167	0.385000	5.000000	67	232.804840	0.682500

4590 rows × 11 columns

Turno 2: time interval: 18 days 10:09:53, interval: 109.79 h, total_weight: 2242139 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
4591 2021-08-25 08:14:00  Parado    86       0       0.0       180.6   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
4591            0.0       0.0        91          313.95                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
4766	2021-08-25 12:01:32	Carregando	81	532	0.00	171.745750	0.012278	0.283333	79	275.414239	0.042500
4767	2021-08-25 12:01:50	Carregando	81	524	0.00	171.732750	0.013000	0.300000	79	275.364239	0.050000
4768	2021-08-25 12:02:07	Carregando	81	599	0.00	171.720472	0.012278	0.283333	79	275.307572	0.056667
4769	2021-08-25 12:09:07	Movimento	81	0	1.07	171.200472	0.520000	7.830000	79	274.001267	1.306305
4770	2021-08-25 12:15:07	Descarregando	81	0	0.00	170.822472	0.378000	6.000000	79	272.909267	1.092000

4771 rows × 11 columns

Turno 1: time interval: 19 days 04:22:07, interval: 113.94 h, total_weight: 2329960 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
4948	2021-08-25 17:36:40	Carregando	76	593	0.00	159.721639	0.004889	0.183333	67	232.286485	0.027500
4949	2021-08-25 17:36:52	Carregando	76	566	0.00	159.716306	0.005333	0.200000	67	232.256485	0.030000
4950	2021-08-25 17:37:07	Carregando	76	571	0.00	159.709639	0.006667	0.250000	67	232.214818	0.041667
4951	2021-08-25 17:47:07	Movimento	75	0	1.24	159.169639	0.540000	10.190000	66	230.514787	1.700032
4952	2021-08-25 17:50:07	Descarregando	75	0	0.00	158.933389	0.236250	3.000000	66	230.059787	0.455000

4953 rows × 11 columns

Turno 2: time interval: 19 days 09:57:07, interval: 118.3 h, total_weight: 2419378 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
4954 2021-08-26 09:06:00  Parado    86       0       0.0       180.6   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
4954            0.0       0.0        89          307.05                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
5117	2021-08-26 12:25:59	Carregando	81	606	0.00	170.646861	0.005000	0.300000	79	274.529084	0.045000
5118	2021-08-26 12:26:13	Carregando	81	516	0.00	170.642972	0.003889	0.233333	79	274.486306	0.042778
5119	2021-08-26 12:26:23	Carregando	81	610	0.00	170.640194	0.002778	0.166667	79	274.452973	0.033333
5120	2021-08-26 12:36:23	Movimento	80	0	1.50	169.950194	0.690000	10.590000	79	273.007438	1.445535
5121	2021-08-26 12:40:23	Descarregando	80	0	0.00	169.803194	0.147000	4.000000	78	272.461438	0.546000

5122 rows × 11 columns

Turno 1: time interval: 20 days 04:47:23, interval: 121.98 h, total_weight: 2500903 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
5279	2021-08-26 17:30:30	Carregando	76	532	0.00	160.548194	0.011250	0.250000	68	235.124743	0.045833
5280	2021-08-26 17:30:45	Carregando	76	508	0.00	160.536944	0.011250	0.250000	68	235.083076	0.041667
5281	2021-08-26 17:31:01	Carregando	76	622	0.00	160.524944	0.012000	0.266667	68	235.038632	0.044444
5282	2021-08-26 17:39:01	Movimento	76	0	1.19	160.074944	0.450000	8.710000	67	233.453412	1.585220
5283	2021-08-26 17:45:01	Descarregando	76	0	0.00	159.623444	0.451500	6.000000	67	232.634412	0.819000

5284 rows × 11 columns

Turno 2: time interval: 20 days 09:52:01, interval: 125.95 h, total_weight: 2578771 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
5285 2021-08-27 09:33:00  Parado    87       0       0.0       182.7   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
5285            0.0       0.0        92           317.4                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
5453	2021-08-27 13:34:23	Carregando	82	564	0.00	172.606639	0.008333	0.250000	79	274.730489	0.045833
5454	2021-08-27 13:34:32	Carregando	82	607	0.00	172.601639	0.005000	0.150000	79	274.707989	0.022500
5455	2021-08-27 13:34:44	Carregando	82	579	0.00	172.594972	0.006667	0.200000	79	274.674656	0.033333
5456	2021-08-27 13:42:44	Movimento	81	0	1.23	172.034972	0.560000	8.290000	79	273.543071	1.131585
5457	2021-08-27 13:46:44	Descarregando	81	0	0.00	171.712972	0.322000	4.000000	79	272.875738	0.667333

5458 rows × 11 columns

Turno 1: time interval: 21 days 05:53:44, interval: 130.29 h, total_weight: 2663601 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
5620	2021-08-27 18:56:52	Carregando	76	481	0.00	160.792722	0.017944	0.316667	68	234.603049	0.052778
5621	2021-08-27 18:57:10	Carregando	76	500	0.00	160.775722	0.017000	0.300000	67	234.543049	0.060000
5622	2021-08-27 18:57:23	Carregando	76	527	0.00	160.763444	0.012278	0.216667	67	234.510549	0.032500
5623	2021-08-27 19:05:23	Movimento	76	0	1.26	160.303444	0.460000	8.400000	67	233.363949	1.146600
5624	2021-08-27 19:09:23	Descarregando	76	0	0.00	160.009444	0.294000	4.000000	67	232.635949	0.728000

5625 rows × 11 columns

Turno 2: time interval: 21 days 11:16:23, interval: 134.45 h, total_weight: 2745258 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
5626 2021-08-30 09:33:00  Parado    97       0       0.0       203.7   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
5626            0.0       0.0        92           317.4                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
5804	2021-08-30 13:47:08	Carregando	92	536	0.00	194.286444	0.012889	0.266667	79	273.826298	0.040000
5805	2021-08-30 13:47:26	Carregando	92	513	0.00	194.271944	0.014500	0.300000	79	273.776298	0.050000
5806	2021-08-30 13:47:40	Carregando	92	631	0.00	194.260667	0.011278	0.233333	79	273.733520	0.042778
5807	2021-08-30 14:03:40	Movimento	92	0	1.56	193.670667	0.590000	16.140000	78	271.530410	2.203110
5808	2021-08-30 14:06:40	Descarregando	92	0	0.00	193.481667	0.189000	3.000000	78	271.075410	0.455000

5809 rows × 11 columns

Turno 1: time interval: 24 days 06:13:40, interval: 139.12 h, total_weight: 2834442 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
5973	2021-08-30 18:58:44	Carregando	87	546	0.00	184.132472	0.007000	0.233333	68	234.892427	0.038889
5974	2021-08-30 18:58:54	Carregando	87	623	0.00	184.127472	0.005000	0.166667	68	234.859093	0.033333
5975	2021-08-30 18:59:11	Carregando	87	502	0.00	184.118972	0.008500	0.283333	68	234.816593	0.042500
5976	2021-08-30 19:13:11	Movimento	87	0	0.87	183.648972	0.470000	14.500000	67	232.177593	2.639000
5977	2021-08-30 19:19:11	Descarregando	87	0	0.00	183.291972	0.357000	6.000000	67	231.176593	1.001000

5978 rows × 11 columns

Turno 2: time interval: 24 days 11:26:11, interval: 143.38 h, total_weight: 2916543 Kg
Inicio do Turno:             date_created  status  fuel  weight  distance  fuel_level  \
5979 2021-08-31 09:59:00  Parado    88       0       0.0       184.8   

      fuel_comsumed  interval  fuel_esc  fuel_level_esc  fuel_comsumed_esc  
5979            0.0       0.0        91          313.95                0.0  

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
6149	2021-08-31 13:46:24	Carregando	83	525	0.00	174.860833	0.018222	0.266667	80	276.844569	0.053333
6150	2021-08-31 13:46:33	Carregando	83	529	0.00	174.850583	0.010250	0.150000	80	276.817069	0.027500
6151	2021-08-31 13:46:47	Carregando	83	582	0.00	174.834639	0.015944	0.233333	80	276.770402	0.046667
6152	2021-08-31 13:56:47	Movimento	82	0	1.51	174.264639	0.570000	10.300000	79	274.895802	1.874600
6153	2021-08-31 14:01:47	Descarregando	82	0	0.00	174.072139	0.192500	5.000000	79	274.061636	0.834167

6154 rows × 11 columns

Turno 1: time interval: 25 days 06:08:47, interval: 147.52 h, total_weight: 3002511 Kg

	date_created	status	fuel	weight	distance	fuel_level	fuel_comsumed	interval	fuel_esc	fuel_level_esc	fuel_comsumed_esc
0	2021-08-06 07:53:00	Parado	98	0	0.00	190.000000	0.000000	0.000000	95	327.000000	0.000000
1	2021-08-06 07:53:00	Parado	91	0	0.00	191.100000	0.000000	0.000000	93	320.850000	0.000000
2	2021-08-06 08:00:00	Movimento	90	0	0.95	190.690000	0.410000	7.600000	92	319.697333	1.152667
3	2021-08-06 08:00:17	Carregando	90	562	0.00	190.666389	0.023611	0.283333	92	319.645389	0.051944
4	2021-08-06 08:00:26	Carregando	90	597	0.00	190.653889	0.012500	0.150000	92	319.617889	0.027500
...	...	...	...	...	...	...	...	...	...	...	...
6319	2021-08-31 18:52:02	Carregando	78	591	0.00	165.417750	0.001750	0.150000	69	239.416983	0.022500
6320	2021-08-31 18:52:18	Carregando	78	569	0.00	165.414639	0.003111	0.266667	69	239.368094	0.048889
6321	2021-08-31 18:52:37	Carregando	78	583	0.00	165.410944	0.003694	0.316667	69	239.304761	0.063333
6322	2021-08-31 19:08:37	Movimento	78	0	1.32	164.950944	0.460000	16.850000	68	237.004736	2.300025
6323	2021-08-31 19:12:37	Descarregando	78	0	0.00	164.768944	0.182000	4.000000	68	236.276736	0.728000

6324 rows × 11 columns

Turno 2: time interval: 25 days 11:19:37, interval: 151.45 h, total_weight: 3086061 Kg